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Debugging process plays a crucial role in helping students pinpoint their specific learning weaknesses, allowing them to modify their strategies for enhanced academic performance. Notably, changes in pupil dilation serve as an indicator of arousal associated with confronting learning challenges. This physiological response acts as a “physiological footprint” that reflects cognitive engagement, facilitating internally focused cognitive processes such as insight generation and mind-wandering. In this study, we proposed that pupil dilation could be a valuable predictor of students’ metacognitive awareness throughout the debugging process, specifically within an augmented reality (AR) learning environment. The findings revealed significant differences in pupil dilation among students categorized by their varying levels of debugging, which represents a specific dimension of the Metacognitive Awareness Inventory. 

This study explores the application of slouching scores to assess ergonomic posture in augmented reality (AR) environments. Employing Microsoft HoloLens 2 with Xsens motion capture technology, participants engaged in interactive biomechanics tasks, including a practical luggage-lifting exercise. Real-time feedback guided users towards safe posture, emphasizing spinal alignment and reducing physical strain. Slouching scores functioned as quantitative measures of posture quality, establishing a connection between unsafe postures and the requisite postural adjustments. The results illustrate how AR-integrated systems can enhance posture awareness, improve user ergonomics, and promote active learning in both educational and professional settings. 

In the context of student learning, investigating effective feedback mechanisms within augmented reality (AR) learning systems is crucial for better understanding and optimizing study behaviors. This study examines the influence of metacognitive monitoring feedback within an AR setting. Our hypothesis suggests that regularly providing students with feedback on their metacognitive monitoring within an AR learning environment has a beneficial effect on their metacognitive state. The results of the study confirm that frequent exposure to such feedback significantly improves scores on the Metacognitive Awareness Inventory. Essentially, there was a marked increase in the inventory scores of participants who received ongoing feedback, compared to those who only received metacognitive monitoring feedback once after the lecture, particularly in the areas of planning, monitoring comprehension, and debugging strategies. This enhancement is achieved by influencing student calibration by directly impacting their metacognitive state. 

With the growing need for augmented reality (AR) technology, understanding and optimizing study behaviors in AR learning environments has become crucial. However, one major drawback of AR learning is the absence of effective feedback mechanisms for students. To overcome this challenge, we introduced metacognitive monitoring feedback. Additionally, we created a location-based AR learning environment utilizing a real-time indoor tracking system to further enhance student learning. This study focuses on the positive impact of metacognitive monitoring feedback in a location-based AR learning environment. Our hypothesis posits that regularly providing students with feedback on their metacognitive monitoring within this new AR learning system positively influences their metacognitive awareness. The study's findings confirm that frequent exposure to such feedback significantly enhances the Metacognitive Awareness Inventory (MAI) scores. Participants who received continuous feedback demonstrated a significant increase in MAI scores compared to those who received feedback only once after the lecture. This improvement is achieved by influencing student calibration and directly enhancing their metacognitive awareness. 

This study investigates the method for measuring cognitive workload in augmented reality-based biomechanics lectures by analyzing pupil dilation. Using Dikablis Glasses 3 and Microsoft HoloLens, we recorded physiological and subjective data across learning and problem-solving phases. Pupil dilation was normalized and segmented, enabling a comparison of cognitive demands between phases. The results indicated significant correlations between pupil dilation and NASA TLX cognitive demand, particularly in lectures that primarily involved procedural knowledge. These findings suggest that instructional design and content complexity have a significant impact on cognitive load, providing valuable insights for optimizing AR-based learning environments to support cognitive efficiency and student engagement. 

There is an increasing demand for developing new metrics that can effectively measure the physical demand experienced by users in augmented reality (AR) environments. In this study, we evaluated one of the recent metrics, called “slouching score,” in an AR-based biomechanics lecture. This study aims to uncover the correlation between the AR interaction and the physical demand of users in a different setup compared to the earlier study. The slouching score, which evaluates posture changes that may indicate fatigue during AR interactions, is measured using Xsens motion capture equipment. These calculated scores are compared with responses to physical demand assessments surveyed using NASA-TLX questionnaires. One of the key differences between the current study and earlier ones is that participants had to physically move to access the next AR module in earlier studies. In contrast, this time, participants simply needed to click a virtual arrow button to view the next AR modules, eliminating the need for physical movement. Our preliminary findings show correlations between the slouching score from some modules and the NASA-TLX physical demand ratings. 

Augmented reality (AR) is gaining attraction as a valuable aid in training and educational settings. However, the cognitive overload due to the new learning environment may hamper effective learning during the AR sessions. For this reason, monitoring students learning status with an effective metric is required. Distraction rate (DR) is a feature extracted from a student’s eye-tracking coordinates data developed to measure the distracted proportion of a student in an AR learning session (Deay, 2023). In this study, we investigate DR with students’ formative and summative assessment outcomes to validate its effectiveness as a predictor for student performance.Methods: To do this, students learned a topic of biomechanics through several AR modules. The results of quizzes taken after each AR module and their class exam outcomes taken at the end of semesters provide formative and summative evaluation performances, respectively. The data were collected in two years in the same setup. To compute DR, the standard eye-tracking coordinates, called baseline, and those of an observed student are compared. In order to reduce false alarms, two sources of noise are accounted for. First, temporal noise caused by quick deviations from the baseline that only lasts for a short period of time is removed by computing the moving average of eye-tracking curves. Second, spatial noise caused by slight deviations in a student’s sight from the virtual instructor is reduced by applying a threshold to determine whether the deviation is large enough. Finally, the proportion of moving average signals exceeding the threshold is computed. Using mixed effects logistic regression models, this study shows how DR and students' performance are related while considering the year and student variations. To extract DR from eye-tracking data, two parameters should be determined, the window size and threshold. In this study, we carried out a comparison study with several parameter values with respect to the model’s prediction performance to find the best parameter tuning.Key Findings:For the formative performance, the results indicate that DR is a significant predictor for the probability of correct answers. For the summative performance, DR does not show a significant relationship with students’ exam scores, yet the negative regression coefficient of DR can be still found, indicating that the high DR value results in low performance in the exam. It can be interpreted that, due to the time interval between AR learning and exams, even if some students may have not paid much attention during the AR learning sessions, they could catch up on the material later by themselves. Overall, it is found that the exam performance is less sensitive, compared to the quiz performance, to students’ attention paid to AR learning sessions. Accordingly, the relationship between DR and summative performance is likely to be weaker than the case of formative assessment. 

In the digital learning landscape, Augmented Reality (AR) is revolutionizing instructional methodologies. This study shifts focus to explore the impact of AR-based lectures on pupil dilation as a biomarker of mental demand. By analyzing pupil dilation with cognitive load assessment tools like the NASA Task Load Index, we aim to understand the cognitive implications of prolonged exposure to AR in educational settings. We hypothesize that variations in pupil size can be indicative of cognitive load, correlating with the mental demands imposed by AR lectures. Preliminary findings suggest a significant relationship between increased pupil dilation and heightened mental workload during AR engagements. This study highlights a new way to measure cognitive workload in AR environments using pupil dilation data. 

This study examines the ergonomic impact of augmented reality (AR) technologies in educational contexts, with a focus on understanding how prolonged AR engagement affects postural dynamics and physical demands on users. By analyzing slouching scores alongside NASA Task Load Index (TLX) Physical Demand (PD) values, we assess the physical strain experienced by participants during the initial modules of an AR-based lecture series. Our findings demonstrate a notable decline in slouching scores as participants progress through the lecture modules, indicating increased postural deviations. To quantify these effects, we developed a regression model that effectively predicts the physical demands imposed by various AR modules, based on the observed slouching scores. 

In this study, we explore the impact of incorporating a virtual instructor with realistic lip-syncing in an augmented reality (AR) learning environment. The study is particularly focused on understanding if this enhancement can reduce students’ mental workload and improve system usability and performance in AR learning. The research stems from previous feedback indicating that a virtual instructor without facial movements was perceived as “creepy” and “distracting.” The updated virtual instructor includes facial animations, such as blinking and synchronized lip movements, especially during lecture explanations. The study aims to determine if there are significant changes in mental workload and usability differences between the AR systems with and without the enhanced virtual instructor. The study found significant differences in the usability scores in some questions. However, there was no significant difference in the mental workload between them.